Book Image

Transformers for Natural Language Processing

By : Denis Rothman
Book Image

Transformers for Natural Language Processing

By: Denis Rothman

Overview of this book

The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers. The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face. The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets.
Table of Contents (16 chapters)
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The rise of billion-parameter transformer models

The speed at which transformers went from small models trained for NLP tasks to models that require little to no fine-tuning is staggering.

Vaswani et al. (2017) introduced the Transformer, which surpassed CNNs and RNNs on BLEU tasks. Radford et al. (2018) introduced the Generative Pre-Training model (GPT) that could perform downstream tasks with fine-tuning. Devlin et al. (2019) perfected fine-tuning with the BERT model. Radford et al. (2019) went further with GPT-2 models.

Brown et al. (2020) defined a GPT-3 zero-shot approach to transformers that do not require fine-tuning!

At the same time, Wang et al. (2019) created GLUE to benchmark NLP models. But transformer models evolved so quickly that they surpassed human baselines!

Wang et al. (2019, 2020) rapidly created SuperGLUE, set the human baselines much higher, and made the NLU/NLP tasks more challenging. Transformers are rapidly progressing on the SuperGLUE leaderboards...